Enhanced knowledge delivery and attainment using a question answering system

ABSTRACT

A mechanism is provided in a data processing system for presentation delivery. The mechanism delivering a presentation content to a group of users and receives a plurality of questions concerning the presentation content from the group of users. The mechanism stores the plurality of questions in a question history database and clusters the plurality of questions in the question history database into one or more question clusters. The mechanism determines a topic for each of the one or more question clusters to form one or more question topics and generates feedback for updating the presentation content based on the one or more question topics.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for enhancedknowledge delivery and attainment using a question answering system.

With the increased usage of computing networks, such as the Internet,humans are currently inundated and overwhelmed with the amount ofinformation available to them from various structured and unstructuredsources. However, information gaps abound as users try to piece togetherwhat they can find that they believe to be relevant during searches forinformation on various subjects. To assist with such searches, recentresearch has been directed to generating Question and Answer (QA)systems which may take an input question, analyze it, and return resultsindicative of the most probable answer to the input question. QA systemsprovide automated mechanisms for searching through large sets of sourcesof content, e.g., electronic documents, and analyze them with regard toan input question to determine an answer to the question and aconfidence measure as to how accurate an answer is for answering theinput question.

Examples, of QA systems are Siri® from Apple®, Cortana® from Microsoft®,and the IBM Watson™ system available from International BusinessMachines (IBM®) Corporation of Armonk, N.Y. The IBM Watson™ system is anapplication of advanced natural language processing, informationretrieval, knowledge representation and reasoning, and machine learningtechnologies to the field of open domain question answering. The IBMWatson™ system is built on IBM's DeepQA™ technology used for hypothesisgeneration, massive evidence gathering, analysis, and scoring. DeepQA™takes an input question, analyzes it, decomposes the question intoconstituent parts, generates one or more hypothesis based on thedecomposed question and results of a primary search of answer sources,performs hypothesis and evidence scoring based on a retrieval ofevidence from evidence sources, performs synthesis of the one or morehypothesis, and based on trained models, performs a final merging andranking to output an answer to the input question along with aconfidence measure.

SUMMARY

in one illustrative embodiment, a method, in a data processing system,is provided for presentation delivery. The method comprises deliveringpresentation content to a group of users and receiving a plurality ofquestions concerning the presentation content from the group of users.The method further comprises storing the plurality of questions in aquestion history database and clustering the plurality of questions inthe question history database into one or more question clusters. Themethod further comprises determining a topic for each of the one or morequestion clusters to form one or more question topics and generatingfeedback for updating the presentation content based on the one or morequestion topics.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer creation (QA) system in a computer network;

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments are implemented;

FIG. 3 illustrates a QA system pipeline for processing an input questionin accordance with one illustrative embodiment;

FIG. 4 is a block diagram of a question answering system for enhancingknowledge delivery and attainment for delivery of presentation contentin accordance with an illustrative embodiment;

FIG. 5 is a flowchart illustrating operation of a mechanism forenhancing knowledge delivery and attainment for delivery of presentationcontent in accordance with an illustrative embodiment; and

FIG. 6 is a flowchart illustrating operation of a question answeringsystem for enhancing knowledge delivery and attainment for delivery ofpresentation content in accordance with an illustrative embodiment.

DETAILED DESCRIPTION

The illustrative embodiments provide mechanisms for enhancing knowledgedelivery and attainment using a question answering system. As companieswork to reduce travel and training costs, the number of cases in whichit is practical to have an instructor teach in front of a group ofstudents have been severely reduced. Organizations and individuals donot want to spend the time or money to attend an information session ata physical location if an equivalent alternative is available. Sometraining has shifted to distance learning, the online version of aclassroom. In many cases, however, this approach presents challenges.For global organizations, the range of time zones sometimes makes itimpractical to conduct live training sessions over the Internet.

Due to the above and other factors, recorded training sessions, whetheraudio only, audio with slides, or video, have become increasingly moreprevalent. A significant downside to the recorded event, though, is thatthe element of interaction is lost in this process. There are currentlysome methods for increasing interaction with the recording itself, suchas click-able items and built-in quizzes; however, while these may helpfrom an attention perspective, they do not actually allow theinteraction to occur as it would in standard instructor-led training.

For recorded events, help files are necessarily static. Help filescannot be updated in real time during delivery nor can they be easilymodified to link to new or updated material. Help files cannot react toaudience queries and cannot dynamically adjust to audience interest tohelp direct the audience to relevant content. Help tiles cannot do datamining, especially in real time, to help the presenter react if a largeportion of the audience begins asking questions indicating someconfusion or need for clarification. In a recorded setting, help tilesfocus on the process of finding information rather than the process ofgaining knowledge.

In accordance with the illustrative embodiments, a question answering(QA) system provides enriched content- and context-related assistancefor broadcast style or recorded deliveries of presentation content. TheQA system may provide feedback to a live presenter or a presentationsystem delivering a recorded event. The feedback identifies which topicsof the presentation are well-understood by an audience, as well as whichtopics require improved or additional content.

Before beginning the discussion of the various aspects of theillustrative embodiments in more detail, it should first be appreciatedthat throughout this description the term “mechanism” will be used torefer to elements of the present invention that perform variousoperations, functions, and the like. A “mechanism,” as the term is usedherein, may be an implementation of the functions or aspects of theillustrative embodiments in the form of an apparatus, a procedure, or acomputer program product. In the case of a procedure, the procedure isimplemented by one or more devices, apparatus, computers, dataprocessing systems, or the like. In the case of a computer programproduct, the logic represented by computer code or instructions embodiedin or on the computer program product is executed by one or morehardware devices in order to implement the functionality or perform theoperations associated with the specific “mechanism.” Thus, themechanisms described herein may be implemented as specialized hardware,software executing on general purpose hardware, software instructionsstored on a medium such that the instructions are readily executable byspecialized or general purpose hardware, a procedure or method forexecuting the functions, or a combination of any of the above.

The present description and claims may make use of the terms “a”, “atleast one of”, and “one or more of” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples intended tobe non-limiting and are not exhaustive of the various possibilities forimplementing the mechanisms of the illustrative embodiments. It will beapparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the examples provided herein without departing from thespirit and scope of the present invention.

The illustrative embodiments may be utilized in many different types ofdata processing environments. In order to provide a context for thedescription of the specific elements and functionality of theillustrative embodiments, FIGS. 1-3 are provided hereafter as exampleenvironments in which aspects of the illustrative embodiments may beimplemented. It should be appreciated that FIGS. 1-3 are only examplesand are not intended to assert or imply any limitation with regard tothe environments in which aspects or embodiments of the presentinvention may be implemented. Many modifications to the depictedenvironments may be made without departing from the spirit and scope ofthe present invention.

FIGS. 1-3 are directed to describing an example Question Answering (QA)system (also referred to as a Question/Answer system or Question andAnswer system), methodology, and computer program product with which themechanisms of the illustrative embodiments are implemented. As will bediscussed in greater detail hereafter, the illustrative embodiments areintegrated in, augment, and extend the functionality of these QAmechanisms with regard to enhancing knowledge delivery and attainmentfor live or recorded delivery of presentation content.

Thus, it is important to first have an understanding of how question andanswer creation in a QA system is implemented before describing how themechanisms of the illustrative embodiments are integrated in and augmentsuch QA systems. It should be appreciated that the QA mechanismsdescribed in FIGS. 1-3 are only examples and are not intended to stateor imply any limitation with regard to the type of QA mechanisms withwhich the illustrative embodiments are implemented. Many modificationsto the example QA system shown in FIGS. 1-3 may be implemented invarious embodiments of the present invention without departing from thespirit and scope of the present invention.

As an overview, a Question Answering system (QA system) is an artificialintelligence application executing on data processing hardware thatanswers questions pertaining to a given subject-matter domain presentedin natural language. The QA system receives inputs from various sourcesincluding input over a network, a corpus of electronic documents orother data, data from a content creator, information from one or morecontent users, and other such inputs from other possible sources ofinput. Data storage devices store the corpus of data. A content creatorcreates content in a document for use as part of a corpus of data withthe QA system. The document may include any file, text, article, orsource of data for use in the QA system. For example, a QA systemaccesses a body of knowledge about the domain, or subject matter area,e.g., financial domain, medical domain, legal domain, etc., where thebody of knowledge (knowledgebase) can be organized in a variety ofconfigurations, e.g., a structured repository of domain-specificinformation, such as ontologies, or unstructured data related to thedomain, or a collection of natural language documents about the domain.

Content users input questions to the QA system which then answers theinput questions using the content in the corpus of data by evaluatingdocuments, sections of documents, portions of data in the corpus, or thelike. When a process evaluates a given section of a document forsemantic content, the process can use a variety of conventions to querysuch document from the QA system, e.g., sending the query to the QAsystem as a well-formed question which are then interpreted by the QAsystem and a response is provided containing one or more answers to thequestion. Semantic content is content based on the relation betweensignifiers, such as words, phrases, signs, and symbols, and what theystand for, their denotation, or connotation. In other words, semanticcontent is content that interprets an expression, such as by usingNatural Language Processing.

As will be described in greater detail hereafter, the QA system receivesan input question, parses the question to extract the major features ofthe question, uses the extracted features to formulate queries, and thenapplies those queries to the corpus of data. Based on the application ofthe queries to the corpus of data, the QA system generates a set ofhypotheses, or candidate answers to the input question, by lookingacross the corpus of data for portions of the corpus of data that havesome potential for containing a valuable response to the input question.The QA system then performs deep analysis on the language of the inputquestion and the language used in each of the portions of the corpus ofdata found during the application of the queries using a variety ofreasoning algorithms. There may be hundreds or even thousands ofreasoning algorithms applied, each of which performs different analysis,e.g., comparisons, natural language analysis, lexical analysis, or thelike, and generates a score. For example, some reasoning algorithms maytook at the matching of terms and synonyms within the language of theinput question and the found portions of the corpus of data. Otherreasoning algorithms may look at temporal or spatial features in thelanguage, white others may evaluate the source of the portion of thecorpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the QA system. The statisticalmodel is used to summarize a level of confidence that the QA system hasregarding the evidence that the potential response, i.e. candidateanswer, is inferred by the question. This process is repeated for eachof the candidate answers until the QA system identifies candidateanswers that surface as being significantly stronger than others andthus, generates a final answer, or ranked set of answers, for the inputquestion.

As mentioned above, QA systems and mechanisms operate by accessinginformation from a corpus of data or information (also referred to as acorpus of content), analyzing it, and then generating answer resultsbased on the analysis of this data. Accessing information from a corpusof data typically includes: a database query that answers questionsabout what is in a collection of structured records, and a search thatdelivers a collection of document links in response to a query against acollection of unstructured data (text, markup language, etc.).Conventional question answering systems are capable of generatinganswers based on the corpus of data and the input question, verifyinganswers to a collection of questions for the corpus of data, correctingerrors in digital text using a corpus of data, and selecting answers toquestions from a pool of potential answers, i.e. candidate answers.

Content creators, such as article authors, electronic document creators,web page authors, document database creators, and the like, determineuse cases for products, solutions, and services described in suchcontent before writing their content. Consequently, the content creatorsknow what questions the content is intended to answer in a particulartopic addressed by the content. Categorizing the questions, such as interms of roles, type of information, tasks, or the like, associated withthe question, in each document of a corpus of data allows the QA systemto more quickly and efficiently identify documents containing contentrelated to a specific query. The content may also answer other questionsthat the content creator did not contemplate that may be useful tocontent users. The questions and answers may be verified by the contentcreator to be contained in the content for a given document. Thesecapabilities contribute to improved accuracy, system performance,machine learning, and confidence of the QA system. Content creators,automated tools, or the like, annotate or otherwise generate metadatafor providing information useable by the QA system to identify thesequestion and answer attributes of the content.

Operating on such content, the QA system generates answers for inputquestions using a plurality of intensive analysis mechanisms whichevaluate the content to identify the most probable answers, i.e.candidate answers, for the input question. The most probable answers areoutput as a ranked listing of candidate answers ranked according totheir relative scores or confidence measures calculated duringevaluation of the candidate answers, as a single final answer having ahighest ranking score or confidence measure, or which is a best match tothe input question, or a combination of ranked listing and final answer.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer creation (QA) system 100 in a computer network 102. Oneexample of a question/answer generation which may be used in conjunctionwith the principles described herein is described in U.S. PatentApplication Publication No. 2011/0125734, which is herein incorporatedby reference in its entirety. The QA system 100 is implemented on one ormore computing devices 104 (comprising one or more processors and one ormore memories, and potentially any other computing device elementsgenerally known in the art including buses, storage devices,communication interfaces, and the like) connected to the computernetwork 102. The network 102 includes multiple computing devices 104 incommunication with each other and with other devices or components viaone or more wired and/or wireless data communication links, where eachcommunication link comprises one or more of wires, routers, switches,transmitters, receivers, or the like. The QA system 100 and network 102enables question/answer (QA) generation functionality for one or more QAsystem users via their respective computing devices 110-112. Otherembodiments of the QA system 100 may be used with components, systems,sub-systems, and/or devices other than those that are depicted herein.

The QA system 100 is configured to implement a QA system pipeline 108that receive inputs from various sources. For example, the QA system 100receives input from the network 102, a corpus of electronic documents106, QA system users, and/or other data and other possible sources ofinput. In one embodiment, some or all of the inputs to the QA system 100are routed through the network 102. The various computing devices 104 onthe network 102 include access points for content creators and QA systemusers. Some of the computing devices 104 include devices for a databasestoring the corpus of data 106 (which is shown as a separate entity inFIG. 1 for illustrative purposes only). Portions of the corpus of data106 may also be provided on one or more other network attached storagedevices, in one or more databases, or other computing devices notexplicitly shown in FIG. 1. The network 102 includes local networkconnections and remote connections in various embodiments, such that theQA system 100 may operate in environments of any size, including localand global, e.g., the Internet.

In one embodiment, the content creator creates content in a document ofthe corpus of data 106 for use as part of a corpus of data with the QAsystem 100. The document includes any file, text, article, or source ofdata for use in the QA system 100. QA system users access the QA system100 via a network connection or an Internet connection to the network102, and input questions to the QA system 100 that are answered by thecontent in the corpus of data 106. In one embodiment, the questions areformed using natural language. The QA system 100 parses and interpretsthe question, and provides a response to the QA system user, e.g., QAsystem user 110, containing one or more answers to the question. In someembodiments, the QA system 100 provides a response to users in a rankedlist of candidate answers while in other illustrative embodiments, theQA system 100 provides a single final answer or a combination of a finalanswer and ranked listing of other candidate answers.

The QA system 100 implements a QA system pipeline 108 which comprises aplurality of stages for processing an input question and the corpus ofdata 106. The QA system pipeline 108 generates answers for the inputquestion based on the processing of the input question and the corpus ofdata 106. The QA system pipeline 108 will be described in greater detailhereafter with regard to FIG. 3.

In some illustrative embodiments, the QA system 100 may be the IBMWatson™ QA system available from International Business MachinesCorporation of Armonk, N.Y., which is augmented with the mechanisms ofthe illustrative embodiments described hereafter. As outlinedpreviously, the IBM Watson™ QA system receives an input question whichit then parses to extract the major features of the question, that inturn are then used to formulate queries that are applied to the corpusof data. Based on the application of the queries to the corpus of data,a set of hypotheses, or candidate answers to the input question, aregenerated by looking across the corpus of data for portions of thecorpus of data that have some potential for containing a valuableresponse to the input question. The IBM Watson™ QA system then performsdeep analysis on the language of the input question and the languageused in each of the portions of the corpus of data found during theapplication of the queries using a variety of reasoning algorithms. Thescores obtained from the various reasoning algorithms are then weightedagainst a statistical model that summarizes a level of confidence thatthe IBM Watson™ QA system has regarding the evidence that the potentialresponse, i.e. candidate answer, is inferred by the question. Thisprocess is be repeated for each of the candidate answers to generateranked listing of candidate answers which may then be presented to theuser that submitted the input question, or from which a final answer isselected and presented to the user. More information about the IBMWatson™ QA system may be obtained, for example, from the IBM Corporationwebsite, IBM Redbooks, and the like. For example, information about theIBM Watson™ QA system can be found in Yuan et al., “Watson andHealthcare,” IBM developerWorks, 2011 and “The Era of Cognitive Systems:An Inside Look at IBM Watson and How it Works” by Rob High, IBMRedbooks, 2012.

In accordance with an illustrative embodiment, a presentation systemdelivers live or recorded presentation content from a server 104 tousers at clients 110, 112. QA system users at clients 110, 112 interactwith QA system 100 during the presentation to ask questions, or simplymake comments, about the presentation to enhance the presentationcontent without necessarily disrupting the presentation flow. Users mayinteract with QA system 100 in a manner similar to a chat session. Inalternative embodiments, QA system 100 may receive questions by voice orvideo messages. QA system 100 provides answers to questions and/orsupplemental content not included in the presentation content within thescope of the presented topic.

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments are implemented. Data processingsystem 200 is an example of a computer, such as server 104 or client 110in FIG. 1, in which computer usable code or instructions implementingthe processes for illustrative embodiments of the present invention arelocated. In one illustrative embodiment, FIG. 2 represents a servercomputing device, such as a server 104, which, which implements QAsystem 100 and QA system pipeline 108 augmented to include theadditional mechanisms of the illustrative embodiments describedhereafter.

In the depicted example, data processing system 200 employs a hubarchitecture including north bridge and memory controller hub (NB/MCH)202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 areconnected to NB/MCH 202. Graphics processor 210 is connected to NB/MCH202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connectsto SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive230, universal serial bus (USB) ports and other communication ports 232,and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus240. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbasic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD226 and CD-ROM drive 230 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 236 is connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within the dataprocessing system 200 in FIG. 2. As a client, the operating system is acommercially available operating system such as Microsoft® Windows 8®.An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 200.

As a server, data processing system 200 may be, for example, an IBM®eServer™ System p® computer system, running the Advanced InteractiveExecutive (AIX®) operating system or the LINUX® operating system. Dataprocessing system 200 may be a symmetric multiprocessor (SMP) systemincluding a plurality of processors in processing unit 206.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 226, and are loaded into main memory 208 for execution byprocessing unit 206. The processes for illustrative embodiments of thepresent invention are performed by processing unit 206 using computerusable program code, which is located in a memory such as, for example,main memory 208, ROM 224, or in one or more peripheral devices 226 and230, for example,

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, iscomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodern 222 or network adapter 212 of FIG. 2, includes one or moredevices used to transmit and receive data. A memory may be, for example,main memory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG.2.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIGS. 1 and 2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS. 1and 2. Also, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system, other than the SMPsystem mentioned previously, without departing from the spirit and scopeof the present invention.

Moreover, the data processing system 200 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 200 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 200 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 3 illustrates a QA system pipeline for processing an input questionin accordance with one illustrative embodiment. The QA system pipelineof FIG. 3 may be implemented, for example, as QA system pipeline 108 ofQA system 100 in FIG. 1. It should be appreciated that the stages of theQA system pipeline shown in FIG. 3 are implemented as one or moresoftware engines, components, or the like, which are configured withlogic for implementing the functionality attributed to the particularstage. Each stage is implemented using one or more of such softwareengines, components or the like. The software engines, components, etc.are executed on one or more processors of one or more data processingsystems or devices and utilize or operate on data stored in one or moredata storage devices, memories, or the like, on one or more of the dataprocessing systems. The QA system pipeline of FIG. 3 is augmented, forexample, in one or more of the stages to implement the improvedmechanism of the illustrative embodiments described hereafter,additional stages may be provided to implement the improved mechanism,or separate logic from the pipeline 300 may be provided for interfacingwith the pipeline 300 and implementing the improved functionality andoperations of the illustrative embodiments.

As shown in FIG. 3, the QA system pipeline 300 comprises a plurality ofstages 310-380 through which the QA system operates to analyze an inputquestion and generate a final response. In an initial question inputstage 310, the QA system receives an input question that is presented ina natural language format. That is, a user inputs, via a user interface,an input question for which the user wishes to obtain an answer, e.g.,“Who are Washington's closest advisors?” In response to receiving theinput question, the next stage of the QA system pipeline 300, i.e. thequestion and topic analysis stage 320, parses the input question usingnatural language processing (NLP) techniques to extract major featuresfrom the input question, and classify the major features according totypes, e.g., names, dates, or any of a plethora of other defined topics.For example, in the example question above, the term “who” may beassociated with a topic for “persons” indicating that the identity of aperson is being sought, “Washington” may be identified as a proper nameof a person with which the question is associated, “closest” may beidentified as a word indicative of proximity or relationship, and“advisors” may be indicative of a noun or other language topic.

In addition, the extracted major features include key words and phrasesclassified into question characteristics, such as the focus of thequestion, the lexical answer type (LAT) of the question, and the like.As referred to herein, a lexical answer type (LAT) is a word in, or aword inferred from, the input question that indicates the type of theanswer, independent of assigning semantics to that word. For example, inthe question “What maneuver was invented in the 1500s to speed up thegame and involves two pieces of the same color?,” the LAT is the string“maneuver.” The focus of a question is the part of the question that, ifreplaced by the answer, makes the question a standalone statement. Forexample, in the question “What drug has been shown to relieve thesymptoms of ADD with relatively few side effects?,” the focus is “drug”since if this word were replaced with the answer, e.g., the answer“Adderall” can be used to replace the term “drug” to generate thesentence “Adderall has been shown to relieve the symptoms of ADD withrelatively few side effects.” The focus often, but not always, containsthe LAT. On the other hand, in many cases it is not possible to infer ameaningful LAT from the focus.

Referring again to FIG. 3, the identified major features are then usedduring the question decomposition stage 330 to decompose the questioninto one or more queries that are applied to the corpora ofdata/information 345 in order to generate one or more hypotheses. Thequeries are generated in any known or later developed query language,such as the Structure Query Language (SQL), or the like. The queries areapplied to one or more databases storing information about theelectronic texts, documents, articles, websites, and the like, that makeup the corpora of data/information 345. That is, these various sourcesthemselves, different collections of sources, and the like, represent adifferent corpus 347 within the corpora 345. There may be differentcorpora 347 defined for different collections of documents based onvarious criteria depending upon the particular implementation. Forexample, different corpora may be established for different topics,subject matter categories, sources of information, or the like. As oneexample, a first corpus may be associated with healthcare documentswhile a second corpus may be associated with financial documents.Alternatively, one corpus may be documents published by the U.S.Department of Energy while another corpus may be IBM Redbooks documents.Any collection of content having some similar attribute may beconsidered to be a corpus 347 within the corpora 345.

The queries are applied to one or more databases storing informationabout the electronic texts, documents, articles, websites, and the like,that make up the corpus of data/information, e.g., the corpus of data106 in FIG. 1. The queries are applied to the corpus of data/informationat the hypothesis generation stage 340 to generate results identifyingpotential hypotheses for answering the input question, which can then beevaluated. That is, the application of the queries results in theextraction of portions of the corpus of data/information matching thecriteria of the particular query. These portions of the corpus are thenanalyzed and used, during the hypothesis generation stage 340, togenerate hypotheses for answering the input question. These hypothesesare also referred to herein as “candidate answers” for the inputquestion. For any input question, at this stage 340, there may behundreds of hypotheses or candidate answers generated that may need tobe evaluated.

The QA system pipeline 300, in stage 350, then performs a deep analysisand comparison of the language of the input question and the language ofeach hypothesis or “candidate answer,” as well as performs evidencescoring to evaluate the likelihood that the particular hypothesis is acorrect answer for the input question. As mentioned above, this involvesusing a plurality of reasoning algorithms, each performing a separatetype of analysis of the language of the input question and/or content ofthe corpus that provides evidence in support of, or not in support ofthe hypothesis. Each reasoning algorithm generates a score based on theanalysis it performs which indicates a measure of relevance of theindividual portions of the corpus of data/information extracted byapplication of the queries as well as a measure of the correctness ofthe corresponding hypothesis, i.e. a measure of confidence in thehypothesis. There are various ways of generating such scores dependingupon the particular analysis being performed. In generally, however,these algorithms look for particular terms, phrases, or patterns of textthat are indicative of terms, phrases, or patterns of interest anddetermine a degree of matching with higher degrees of matching beinggiven relatively higher scores than lower degrees of matching.

Thus, for example, an algorithm may be configured to took for the exactterm from an input question or synonyms to that term in the inputquestion, e.g., the exact term or synonyms for the term “movie,” andgenerate a score based on a frequency of use of these exact terms orsynonyms. In such a case, exact matches will be given the highestscores, while synonyms may be given tower scores based on a relativeranking of the synonyms as may be specified by a subject matter expert(person with knowledge of the particular domain and terminology used) orautomatically determined from frequency of use of the synonym in thecorpus corresponding to the domain. Thus, for example, an exact match ofthe term “movie” in content of the corpus (also referred to as evidence,or evidence passages) is given a highest score. A synonym of movie, suchas “motion picture” may be given a lower score but still higher than asynonym of the type “film” or “moving picture show.” Instances of theexact matches and synonyms for each evidence passage may be compiled andused in a quantitative function to generate a score for the degree ofmatching of the evidence passage to the input question.

Thus, for example, a hypothesis or candidate answer to the inputquestion of “What was the first movie?” is “The Horse in Motion.” theevidence passage contains the statements “The first motion picture evermade was ‘The Horse in Motion’ in 1878 by Eadweard Muybridge. It was amovie of a horse running,” and the algorithm is looking for exactmatches or synonyms to the focus of the input question, i.e. “movie,”then an exact match of “movie” is found in the second sentence of theevidence passage and a highly scored synonym to “movie,” i.e. “motionpicture,” is found in the first sentence of the evidence passage. Thismay be combined with further analysis of the evidence passage toidentify that the text of the candidate answer is present in theevidence passage as well, i.e. “The Horse in Motion,” These factors maybe combined to give this evidence passage a relatively high score assupporting evidence for the candidate answer “The Horse in Motion” beinga correct answer.

It should be appreciated that this is just one simple example of howscoring can be performed. Many other algorithms of various complexitymay be used to generate scores for candidate answers and evidencewithout departing from the spirit and scope of the present invention.

In the synthesis stage 360, the large number of scores generated by thevarious reasoning algorithms are synthesized into confidence scores orconfidence measures for the various hypotheses. This process involvesapplying weights to the various scores, where the weights have beendetermined through training of the statistical model employed by the QAsystem and/or dynamically updated. For example, the weights for scoresgenerated by algorithms that identify exactly matching terms and synonymmay be set relatively higher than other algorithms that are evaluatingpublication dates for evidence passages. The weights themselves may bespecified by subject matter experts or learned through machine learningprocesses that evaluate the significance of characteristics evidencepassages and their relative importance to overall candidate answergeneration.

The weighted scores are processed in accordance with a statistical modelgenerated through training of the QA system that identifies a manner bywhich these scores may be combined to generate a confidence score ormeasure for the individual hypotheses or candidate answers. Thisconfidence score or measure summarizes the level of confidence that theQA system has about the evidence that the candidate answer is inferredby the input question, i.e. that the candidate answer is the correctanswer for the input question,

The resulting confidence scores or measures are processed by a finalconfidence merging and ranking stage 370 which compares the confidencescores and measures to each other, compares them against predeterminedthresholds, or performs any other analysis on the confidence scores todetermine which hypotheses/candidate answers are the most likely to bethe correct answer to the input question. The hypotheses/candidateanswers are ranked according to these comparisons to generate a rankedlisting of hypotheses/candidate answers (hereafter simply referred to as“candidate answers”). From the ranked listing of candidate answers, atstage 380, a final answer and confidence score, or final set ofcandidate answers and confidence scores, are generated and output to thesubmitter of the original input question via a graphical user interfaceor other mechanism for outputting information.

FIG. 4 is a block diagram of a question answering system for enhancingknowledge delivery and attainment for delivery of presentation contentin accordance with an illustrative embodiment. A presenter orpresentation system delivers presentation content 403 to a group ofusers. Question answering (QA) system 410 receives questions 401 fromthe group of users who are viewing or listening to a live or recordedpresentation. QA system 410 provides answers to questions 401 in theform of supplemental information 416 based on information in corpus 404and, in one embodiment, presentation content 403. QA system 410 mayreturn supplemental information 416 to the users asking the question.

QA system 410 may access presentation content 403 by interfacing with apresentation system. Corpus 404 may also include information fromtraining oriented material from internal and possibly external sitesdepending on the presentation topic. The training oriented material mayinclude whitepapers, presentations, etc. QA system 410 may activelylisten to live or recorded presentations. QA system 410 may be connectedin the background with context to any video or audio prompts that may berelevant shortly before a question is asked.

QA system 410 includes reasoning algorithm (RA) pipeline 411, whichgenerates question features 412 from questions 401 and answer features413 from candidate answers generated from corpus 404 and/or presentationcontent 403. Question features 412 may include, for example, LexicalAnswer Type (LAT), focus, question classification, etc. Answer features413 may include, for example, sources of evidentiary support foranswers, answer confidence, etc. QA system 410 stores questioninformation in question history database (DB) 405. The questioninformation may include the question text, question features, highconfidence answers, sources of evidentiary support, identification ofthe user and/or user group, etc.

Clustering component 414 performs clustering on received questions 401,as well as questions in question history DB 405. Cluster analysis orclustering is the task of grouping a set of objects in such a way thatobjects in the same group (i.e., a duster) are more similar in somesense or another to each other than to those in other groups (clusters).Clustering is a main task of exploratory data mining, and a commontechnique for statistical data analysis, used in many fields, includingmachine learning, pattern recognition, image analysis, informationretrieval, and bioinformatics.

In one example embodiment, clustering component 414 uses k-meansclustering. The k-means clustering algorithm is a technique of vectorquantization, originally from signal processing, that is popular forcluster analysis in data mining. The k-means clustering algorithm aimsto partition n observations into k clusters in which each observationbelongs to the cluster with the nearest mean, serving as a prototype ofthe cluster.

Clustering component 414 groups questions into question clusters basedon similarity of question features 412 and answer features 413. In oneexample embodiment, clustering component 414 stores information aboutquestion clusters in question history DB 405. In one embodiment, QAsystem 410 uses capabilities of RA pipeline 411 to identify a topic ofthe questions. For example, consider a presentation about changes tohealth care law. Users viewing the presentation may ask questions aboutthe subject matter being presented. One or two users may ask about aparticular state's insurance exchange, but many users may ask aboutchildren's dental coverage. In this example, clustering component 414clusters the questions about the topic of “state-run insurance exchange”in one cluster and the questions about “children's dental coverage” inanother cluster.

Feedback generating component 415 generates presentation feedback 417based on the question clusters determined by clustering component 414.Feedback generating component 415 provides presentation feedback 417 tothe presenter or presentation system.

In one embodiment, feedback generating component 415 generatespresentation feedback 417 according to a number of questions in eachcluster. That is for questions that have been asked only a few times,feedback generating component 415 may simply provide supplementalinformation 416 to the users asking the questions without interruptingor modifying the presentation. However, in response to a number ofquestions in a cluster exceeding a first threshold, feedback generatingcomponent 415 generates feedback for updating future presentationcontent to include supplemental information 416 for the given questiontopic not contained in the presentation content 403. In response to thenumber of questions in the cluster exceeding a second threshold,feedback generating component 415 generates feedback for updatingcurrent presentation content 403 to include supplemental information 416for the given question topic not contained in the presentation content403.

In an example embodiment, feedback generating component 415 accessespresentation content 403 to determine whether supplemental information416 is present in presentation content 403. If feedback generatingcomponent 415 determines that a cluster of questions will be answeredlater in the presentation, then feedback generating component 415 doesnot update current or future presentation content. However, if feedbackgenerating component 415 determines that users are asking questions forwhich presentation content 403 does not provide support, then feedbackgenerating component 415 generates presentation feedback 417 to updatepresentation content 403.

In an illustrative embodiment, users or groups of users provide userprofile 402. The creator of the presentation may customize presentationcontent 403 for a particular group of users based on information in userprofiles 402. That is, the creator of presentation content 403 maydetermine that a group of users have sufficient understanding of sometopics and need deeper knowledge for other topics based on user profiles402.

As QA system 410 receives questions 401 and clustering component 414groups questions 401 into clusters, feedback generating component 415may update user profiles 402. That is, if users begin asking a highnumber of questions about a particular topic, feedback generatingcomponent 415 may determine that the group of users require moreinformation about that topic. Thus, feedback generating component 415updates user profiles 402 to indicate that the group of users requiremore information about the identified topic. In an alternativeembodiment, QA system 410 ingests profile information from user profiles402 and stores the information in question history DB 405.

The features of QA system 410, as described above with respect to FIG.4, may be used in many ways. In most live broadcast presentations, thereis a concurrent chat session for the audience to post questions and forthe conference moderator to post information. Leveraging this interface,QA system 410 would act in the moderator role providing faster answersto questions and optionally providing reference links for additionalrelated content, such as whitepapers, presentations, videos, or othercontent on a relevant topic. This reference information could beprovided in a verbose chat session or accumulated and later posted withthe content of the live presentation.

QA system 410 could discretely provide feedback to the presenter toassist with positioning or delivery of the presentation content. Chatcomments may provide insight that the audience needs more thanexplanation or that there is a strong interest in subject matter thatwas not planned to receive significant attention. QA system 410 couldprovide cues to the presenter along with recommended content to redirectthe presentation accordingly. The presenter may still elect to stay thecourse of the presentation or may lightly or heavily leverage therecommended content to better satisfy the audience based on the level ofinterest, based on similar questions or a quick poll.

QA system 410 may redirect the flow of a presentation based on aweighted average of the expertise of the current audience or from thetypes of questions being asked. QA system 410 receives input from theaudience of a replay of a presentation and may control the presentationitself.

During replay of a presentation, QA system 410 may harvest questionsfrom viewers and provide answers and references as appropriate thatwould enhance the value of the answer. As with a live presentation witha chat session, a context sensitive chat environment with QA system 410can be leveraged to enable better relevancy for the presumably smalleraudience.

QA system 410 may allow the replay of presentation content to flowdifferently based on the types of questions asked in a chat session. QAsystem 410 would answer new chat questions even for a replayed session.An individual or smaller group of users watching a replay may havedifferent needs than the larger audience that attended the live session.For example, someone who participated in the live session may want tobuild deeper insights on a specific set of topics. Providing keywords ortags to the QS system 410 may trigger a customized abbreviated view ofthe material and cause new recommended content to be suggested forfurther study.

QA system 410 may allow viewers of a replay to choose one's own customsession based on current knowledge level. An individual could leveragethe replay session as part of a broader knowledge need. QA system 410would utilize assets and integrate them to provide a set of informationand recommended approach to study, serving up information as a highlevel overview, building block approach, lecture series, or anotherappropriate structured format.

QA system 410 may conduct a pre-presentation survey of invitees tobetter determine the content or how the presentation should flow.Typically, presentation material is a small subset of the availablepresentation content in order that the presentation material fits into acertain time format, for example in a one-hour time slot. At least a fewdays prior to the live presentation, QA system 410 would collect briefsurvey information from the prospective audience to help the presentertailor the presentation or be better prepared for some deeper questionsthat may arise.

QA system 410 may provide auto-generated content (e.g., presentation,help file, video) of information based on initial topic and questionsasked during replay. In the absence of known existing presentations, auser can initiate an education advisor interface providing a topic setor short curriculum and receive a structured reference to assets tomethodically leverage content to build knowledge. Building a worthwhilerecommendation would likely require a conversational interface thatcollects details about scope, depth, time focus, and calendar time, aswell as other parameters to support the objective.

A product delivered with a presentation and QA system 410 can replace aninstallation or configuration guide. Such a presentation could beinteractive based on metadata or learned experience of a user. Insteadof step-by-step instructions on paper, QA system 410 can generate a setof dynamic content delivered to a smartphone, tablet, or similar devicethat would include up-to-date detailed instructions utilizingdemonstration videos, pictures and diagrams, audio delivered content,and helpful references for more information. This content would bedevice and data bandwidth appropriate.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the users computer and partly on a remote computer orentirely on the remote computer or server. In the latter scenario, theremote computer may be connected to the user's computer through any typeof network, including a local area network (LAN) or a wide area network(WAN), or the connection may be made to an external computer (forexample, through the Internet using an Internet Service Provider). Insome embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

FIG. 5 is a flowchart illustrating operation of a mechanism forenhancing knowledge delivery and attainment for delivery of presentationcontent in accordance with an illustrative embodiment. Operation begins(block 500), and the mechanism presents content (block 501). Themechanism may present the content as a live content broadcast (block502) or a replay of a content broadcast (block 503). Thereafter, themechanism obtains user profiles, which specify user interests (block504).

Then, the mechanism determines whether a question is received (block505). If the mechanism does not receive a question, the mechanismdetermines whether the presentation is finished (block 506). If thepresentation is not finished, operation returns to block 501 to presentcontent.

If the mechanism receives a question in block 505, the mechanismprocesses the question using a question answering system (block 507).Operation of a question answering system is described in detail withreference to FIGS. 1-3. Next, the mechanism updates a knowledge base(block 508). The knowledge base may comprise question history DB 405 andcluster information generated by clustering component 414 in FIG. 4. Inone embodiment, updating the knowledge base may comprise updating userprofiles in block 504.

The mechanism then provides feedback to the presenter or presentationsystem (block 509). In one embodiment, the feedback includesrecommendations to update presentation content for the current or futurepresentations. The mechanism also provides supplemental information tothe user asking the question (block 510).

Thereafter, the mechanism determines whether the presentation isfinished (block 506). If the mechanism determines the presentation isnot finished, operation returns to block 501 to present content. If themechanism determines the presentation is finished in block 506, thenoperation ends (block 511).

FIG. 6 is a flowchart illustrating operation of a question answeringsystem for enhancing knowledge delivery and attainment for delivery ofpresentation content in accordance with an illustrative embodiment.Operation begins (block 600), and the QA system determines whether aquestion is received (block 601). If the QA system determines that aquestion is not received, the QA system determines whether thepresentation is finished (block 602). If the presentation is notfinished, operation returns to block 601 to determine whether a questionis received. If the QA system determines the presentation is finished inblock 602, then operation ends (block 603).

If the QA system receives a question in block 601, then the QA systemextracts features from the question (block 604). The QA systemdetermines candidate answers to the question (block 605) and extractsfeatures from the answers and source material (block 606). The QA systemclusters the question with a question history (block 607) and determinesa topic of the question cluster (block 608). Then, the QA systemincrements a counter for the topic associated with the question cluster(block 609).

The QA system determines whether the counter is greater than a firstthreshold (THRESHOLD1) (block 610). If the counter is not greater thanthe first threshold, then the QA system presents the answer and/orsupplemental information to the user (block 611). Then, the QA systemupdates a user profile associated with the user or the group of usersviewing the presentation (block 612). Thereafter, operation proceeds toblock 602 to determine whether the presentation is finished.

If the counter is greater than the first threshold in block 610, the QAsystem determines whether the counter is greater than a second threshold(THRESHOLD2) (block 613). If the counter is not greater than the secondthreshold, then the QA system generates feedback to update futurepresentation content (block 614). Then, the QA system presents theanswer and/or supplemental information to the user (block 611) andupdates a user profile associated with the user or the group of usersviewing the presentation (block 612). Thereafter, operation proceeds toblock 602 to determine whether the presentation is finished.

If the counter is greater than the second threshold in block 613, thenthe QA system generates feedback to modify the current presentation(block 615). Then, the QA system generates feedback to update futurepresentation content (block 614), presents the answer and/orsupplemental information to the user (block 611), and updates a userprofile associated with the user or the group of users viewing thepresentation (block 612). Thereafter, operation proceeds to block 602 todetermine whether the presentation is finished.

The flowchart, and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers. Network adapters mayalso be coupled to the system to enable the data processing system tobecome coupled to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modems and Ethernet cards are just a few of the currentlyavailable types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments. The embodiment was chosen and described in order to bestexplain the principles of the invention, the practical application, andto enable others of ordinary skill in the art to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated. The terminology used hereinwas chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method, in a data processing system having aprocessor and a memory, wherein the memory comprises instructions whichare executed by the processor to cause the processor to implement aquestion answering system for presentation delivery, the methodcomprising: delivering a presentation content by a presenter to a groupof users; receiving, by the question answering system, a plurality ofquestions concerning the presentation content from the group of users;for each received question, generating, by a hypothesis generation stageof the question answering system, one or more candidate answers based ona corpus of information and presenting, by the question answeringsystem, the one or more candidate answers to a user that submitted thereceived question; storing, by the question answering system, questioninformation for the plurality of questions in a question historydatabase, wherein the question information comprises question text, oneor more candidate answers, and supplemental information from the corpusof information providing evidentiary support for the one or morecandidate answers; clustering, by a clustering component executingwithin the question answering system, the plurality of questions in thequestion history database into one or more question clusters based onsimilarity of question features and answer features; determining, by thequestion answering system, a topic for each of the one or more questionclusters to form one or more question topics; identifying, by a feedbackgenerating component executing within the question answering system,supplemental information for a given question topic that is in thequestion history database but not contained in the presentation content;generating, by the feedback generating component, feedback based on theone or more question topics, wherein the feedback comprises theidentified supplemental information; and providing, by the questionanswering system, the feedback to the presenter.
 2. The method of claim1, wherein generating feedback comprises generating alerts to thepresenter to alter the presentation based on the one or more questiontopics.
 3. The method of claim 1, wherein clustering the plurality ofquestions comprises: extracting a set of features from each questionwithin the plurality of questions, wherein each feature in the set offeatures has a feature value; and performing a clustering operation onthe plurality of questions to group questions having similar featurevalues.
 4. The method of claim 3, wherein performing the clusteringoperation on the plurality of questions comprises performing a k-meansclustering algorithm on the plurality of questions.
 5. The method ofclaim 3, wherein the set of features comprise a lexical answer type, afocus, and a question classification.
 6. The method of claim 1, whereinpresenting the one or more candidate answers comprises presentingsupplemental information not contained in the presentation content tothe user.
 7. The method of claim 1, wherein clustering the plurality ofquestions comprises: extracting a set of features from each questionwithin the plurality of questions and each candidate answer, whereineach feature in the set of features has a feature value; and performinga clustering operation on the plurality of questions, wherein theclustering operation groups questions having similar feature values. 8.The method of claim 1, wherein generating the feedback comprises: foreach question grouped in a given question topic, incrementing a topiccounter; responsive to the topic counter exceeding a first threshold,generating the feedback identifying the supplemental information for thegiven question topic not contained in the presentation content.
 9. Themethod of claim 8, wherein generating the feedback further comprises:responsive to the topic counter exceeding a second threshold, generatingthe feedback identifying customized presentation content redirecting thedelivery of the presentation content to the group of users based on theone or more question clusters.
 10. The method of claim 1, wherein thepresentation content is customized for the group of users based on oneor more user profiles of the group of users.
 11. The method of claim 10,further comprising updating the one or more user profiles based on theone or more question topics.
 12. The method of claim 11, whereingenerating feedback comprises generating the feedback for the group ofusers based on the one or more updated user profiles.
 13. A computerprogram product comprising a computer readable storage medium having acomputer readable program stored therein, wherein the computer readableprogram, when executed on a computing device, causes the computingdevice to implement a question answering system for presentationdelivery, wherein the computer readable program causes the computingdevice to: deliver a presentation content by a presenter to a group ofusers; receive, by the question answering system, a plurality ofquestions concerning the presentation content from the group of users;for each received question, generate, by a hypothesis generation stageof the question answering system, one or more candidate answers based ona corpus of information and present, by the question answering system, ahighest confidence answer within the one or more candidate answers to auser that submitted the received question; store, by the questionanswering system, question information for the plurality of questions ina question history database, wherein the question information comprisesquestion text, one or more candidate answers, and supplementalinformation from the corpus of information providing evidentiary supportfor the one or more candidate answers; cluster, by a clusteringcomponent executing within the question answering system, the pluralityof questions in the question history database into one or more questionclusters; determine, by the question answering system, a topic for eachof the one or more question clusters to form one or more questiontopics; identify, by a feedback generating component executing withinthe question answering system, supplemental information for a givenquestion topic that is in the question history database but notcontained in the presentation content; generate, by the questionanswering system, feedback based on the one or more question topics,wherein the feedback comprises the identified supplemental information;and provide, by the question answering system, the feedback to thepresenter.
 14. The computer program product of claim 13, whereinclustering the plurality of questions comprises: extracting a set offeatures from each question within the plurality of questions, whereineach feature in the set of features has a feature value; and performinga clustering operation on the plurality of questions, wherein theclustering operation groups questions having similar feature values. 15.The computer program product of claim 13, wherein generating feedbackfor updating the presentation content comprises: for each questiongrouped in a given question topic, incrementing a topic counter;responsive to the topic counter exceeding a first threshold, generatingthe feedback identifying supplemental information for the given questiontopic not contained in the presentation content.
 16. The computerprogram product of claim 15, wherein generating the feedback furthercomprises: responsive to the topic counter exceeding a second threshold,generating the feedback identifying customized presentation contentredirecting the delivery of the presentation content to the group ofusers based on the one or more question clusters.
 17. An apparatuscomprising: a processor; and a memory coupled to the processor, whereinthe memory comprises instructions which, when executed by the processor,cause the processor to implement a question answering system forpresentation delivery, wherein the instructions cause the processor to:deliver a presentation content by a presenter to a group of users;receive, by the question answering system, a plurality of questionsconcerning the presentation content from the group of users; for eachreceived question, generate, by a hypothesis generation stage of thequestion answering system, one or more candidate answers based on acorpus of information and present, by the question answering system, ahighest confidence answer within the one or more candidate answers to auser that submitted the received question; store, by the questionanswering system, question information for the plurality of questions ina question history database, wherein the question information comprisesquestion text, one or more candidate answers, and supplementalinformation from the corpus of information providing evidentiary supportfor the one or more candidate answers; cluster, by a clusteringcomponent executing within the question answering system, the pluralityof questions in the question history database into one or more questionclusters; determine, by the question answering system, a topic for eachof the one or more question clusters to form one or more questiontopics; identify, by a feedback generating component executing withinthe question answering system, supplemental information for a givenquestion topic that is in the question history database but notcontained in the presentation content; generate, by the questionanswering system, feedback based on the one or more question topics,wherein the feedback comprises the identified supplemental information;and provide, by the question answering system, the feedback to thepresenter.
 18. The apparatus of claim 17, wherein clustering theplurality of questions comprises: extracting a set of features from eachquestion within the plurality of questions, wherein each feature in theset of features has a feature value; and performing a clusteringoperation on the plurality of questions, wherein the clusteringoperation groups questions having similar feature values.
 19. Theapparatus of claim 17, wherein generating the feedback comprises: foreach question grouped in a given question topic, incrementing a topiccounter; responsive to the topic counter exceeding a first threshold,generating the feedback identifying the supplemental information for thegiven question topic not contained in the presentation content.
 20. Theapparatus of claim 19, wherein generating the feedback furthercomprises: responsive to the topic counter exceeding a second threshold,generating the feedback identifying customized presentation contentredirecting the delivery of the presentation content to the group ofusers based on the one or more question clusters.